U.S. Pat. No. 7,079,674 B2 discloses a system and method for segmenting cardiac images using a contour propagation model that integrates visual information and anatomical constraints. The propagation model comprises a weighted integration of a boundary segmentation model, a region model and a coupling function.
U.S. Pat. No. 5,239,591 discloses a method of extracting contours in multi-phase multi-slice cardiac magnetic resonance imaging study in response to user input of a seed contour identifying the contour feature to be extracted in an initial image at a middle slice position and a predetermined phase position, namely end of diastole. From this as the only contour inputted by the user, contours are extracted from each image by a sequence of automatic propagation of determinations of extracted or final contours by forming from a final contour for an image a seed contour for a not yet processed image which immediately adjoins in slice or phase position.
Another semi-automatic cardiac contour propagation method is described in G. L. T. F. Hautvast et al., “Automatic cardiac contour propagation in short axis cardiac MR images”, In proc. CARS 2005, Elsevier ICS 1281:351-356, 2005. Here, an automatic cardiac contour propagation method is described based on active contours. The method can be used to propagate cardiac contours that conform to an initial manual segmentation by exploiting information in adjacent images. It is used to delineate the left ventricle endocardium, the left ventricle epicardium and right ventricle endocardium contours.
Further, in G. L. T. F. Hautvast, S. Lobregt, M. Breeuwer and F. A. Gerritsen, “Automatic Contour Propagation in Cine Cardiac Magnetic Resonance Images”, IEEE TMI 25(11): In press, 2006. Here, a method for automatic contour propagation in cine cardiac magnetic resonance images is described. The method consists of an active contour model that tries to maintain a constant contour environment by matching gray values in profiles perpendicular to the contour. Consequently, the contours maintain a constant position with respect to neighboring anatomical structures, such that the resulting contours reflect the preferences of the user. This is particularly important in cine cardiac magnetic resonance images because local image features do not describe the desired contours near the papillary muscle. The accuracy of the propagation result is influenced by several parameters. Because the optimal setting of these parameters is application dependent, it is described how to use full factorial experiments to optimize the parameter setting. Such semi-automatic segmentation tools are capable of propagating cardiac contours manually defined in a functional image at the end diastolic (ED) phase to the other phases in the heart cycle.